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Item A multistate transition model for statin‐induced myopathy and statin discontinuation(Wiley, 2021) Zhu, Yuxi; Chiang, Chien-Wei; Wang, Lei; Brock, Guy; Milks, M. Wesley; Cao, Weidan; Zhang, Pengyue; Zeng, Donglin; Donneyong, Macarius; Li, Lang; Biostatistics and Health Data Science, School of MedicineThe overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.Item Anxiety sensitivity as a transdiagnostic risk factor for trajectories of adverse posttraumatic neuropsychiatric sequelae in the AURORA study(Elsevier, 2022-12) Short , Nicole A.; van Rooij , Sanne J. H.; Murty, Vishnu P.; Stevens, Jennifer S.; An , Xinming; Ji , Yinyao; McLean , Samuel A.; House , Stacey L.; Beaudoin, Francesca L.; Zeng, Donglin; Neylan, Thomas C.; Clifford, Gari D.; Linnstaedt, Sarah D.; Germine, Laura T.; Bollen, Kenneth A.; Rauch , Scott L.; Haran , John P.; Lewandowski, Christopher; Musey Jr., Paul I.; Hendry , Phyllis L.; Sheikh , Sophia; Jones , Christopher W.; Punches, Brittany E.; Swor , Robert A.; McGrath , Meghan E.; Hudak , Lauren A.; Pascual , Jose L.; Seamon , Mark J.; Datner , Elizabeth M.; Pearson , Claire; Peak , David A.; Merchant , Roland C.; Domeier , Robert M.; Rathlev, Niels K.; O'Neil, Brian J.; Sergot, Paulina; Sanchez, Leon D.; Bruce, Steven E.; Pietrzak, Robert H.; Joormann, Jutta; Barch, Deanna M.; Pizzagalli , Diego A.; Sheridan, John F.; Smoller, Jordan W.; Harte, Steven E.; Elliott, James M.; Kessler, Ronald C.; Koenen, Karestan C .; Jovanovic , Tanja; Emergency Medicine, School of MedicineAnxiety sensitivity, or fear of anxious arousal, is cross-sectionally associated with a wide array of adverse posttraumatic neuropsychiatric sequelae, including symptoms of posttraumatic stress disorder, depression, anxiety, sleep disturbance, pain, and somatization. The current study utilizes a large-scale, multi-site, prospective study of trauma survivors presenting to emergency departments. Hypotheses tested whether elevated anxiety sensitivity in the immediate posttrauma period is associated with more severe and persistent trajectories of common adverse posttraumatic neuropsychiatric sequelae in the eight weeks posttrauma. Participants from the AURORA study (n = 2,269 recruited from 23 emergency departments) completed self-report assessments over eight weeks posttrauma. Associations between heightened anxiety sensitivity and more severe and/or persistent trajectories of trauma-related symptoms identified by growth mixture modeling were analyzed. Anxiety sensitivity assessed two weeks posttrauma was associated with severe and/or persistent posttraumatic stress, depression, anxiety, sleep disturbance, pain, and somatic symptoms in the eight weeks posttrauma. Effect sizes were in the small to medium range in multivariate models accounting for various demographic, trauma-related, pre-trauma mental health-related, and personality-related factors. Anxiety sensitivity may be a useful transdiagnostic risk factor in the immediate posttraumatic period identifying individuals at risk for the development of adverse posttraumatic neuropsychiatric sequelae. Further, considering anxiety sensitivity is malleable via brief intervention, it could be a useful secondary prevention target. Future research should continue to evaluate associations between anxiety sensitivity and trauma-related pathology.Item Association between microbiome and the development of adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure(Springer Nature, 2023-11-18) Zeamer, Abigail L.; Salive, Marie-Claire; An, Xinming; Beaudoin, Francesca L.; House, Stacey L.; Stevens, Jennifer S.; Zeng, Donglin; Neylan, Thomas C.; Clifford, Gari D.; Linnstaedt, Sarah D.; Rauch, Scott L.; Storrow, Alan B.; Lewandowski, Christopher; Musey, Paul I., Jr.; Hendry, Phyllis L.; Sheikh, Sophia; Jones, Christopher W.; Punches, Brittany E.; Swor, Robert A.; Hudak, Lauren A.; Pascual, Jose L.; Seamon, Mark J.; Harris, Erica; Pearson, Claire; Peak, David A.; Merchant, Roland C.; Domeier, Robert M.; Rathlev, Niels K.; O’Neil, Brian J.; Sergot, Paulina; Sanchez, Leon D.; Bruce, Steven E.; Kessler, Ronald C.; Koenen, Karestan C.; McLean, Samuel A.; Bucci, Vanni; Haran, John P.; Emergency Medicine, School of MedicinePatients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biological mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition after trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether the gut microbiomes of trauma-exposed emergency department patients who develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms. We performed metagenomic analysis on stool samples (n = 51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Two-, eight- and twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and encoded metabolic pathways from stool metagenomics. Microbial species, including Flavonifractor plautii, Ruminococcus gnavus and, Bifidobacterium species, which are prevalent commensal gut microbes, were found to be important in predicting worse APNS outcomes from microbial abundance data. Notably, through APNS outcome modeling using microbial metabolic pathways, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways. Common commensal microbial species are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, a metabolic change that has also been demonstrated in the plasma of patients with PTSD.Item The AURORA Study: A Longitudinal, Multimodal Library of Brain Biology and Function after Traumatic Stress Exposure(Springer Nature, 2020-02) McLean, Samuel A.; Ressler, Kerry; Koenen, Karestan Chase; Neylan, Thomas; Germine, Laura; Jovanovic, Tanja; Clifford, Gari D.; Zeng, Donglin; An, Xinming; Linnstaedt, Sarah; Beaudoin, Francesca; House, Stacey; Bollen, Kenneth A.; Musey, Paul; Hendry, Phyllis; Jones, Christopher W.; Lewandowski, Christopher; Swor, Robert; Datner, Elizabeth; Mohiuddin, Kamran; Stevens, Jennifer S.; Storrow, Alan; Kurz, Michael Christopher; McGrath, Meghan E.; Fermann, Gregory J.; Hudak, Lauren A.; Gentile, Nina; Chang, Anna Marie; Peak, David A.; Pascual, Jose L.; Seamon, Mark J.; Sergot, Paulina; Peacock, W. Frank; Diercks, Deborah; Sanchez, Leon D.; Rathlev, Niels; Domeier, Robert; Haran, John Patrick; Pearson, Claire; Murty, Vishnu P.; Insel, Thomas R.; Dagum, Paul; Onnela, Jukka-Pekka; Bruce, Steven E.; Gaynes, Bradley N.; Joormann, Jutta; Miller, Mark W.; Pietrzak, Robert H.; Buysse, Daniel J.; Pizzagalli, Diego A.; Rauch, Scott L.; Harte, Steven E.; Young, Larry J.; Barch, Deanna M.; Lebois, Lauren A. M.; van Rooij, Sanne J. H.; Luna, Beatriz; Smoller, Jordan W.; Dougherty, Robert F.; Pace, Thaddeus W. W.; Binder, Elisabeth; Sheridan, John F.; Elliott, James M.; Basu, Archana; Fromer, Menachem; Parlikar, Tushar; Zaslavsky, Alan M.; Kessler, Ronald; Emergency Medicine, School of MedicineAdverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions.Item Brain-Based Biotypes of Psychiatric Vulnerability in the Acute Aftermath of Trauma(American Psychiatric Association, 2021) Stevens, Jennifer S.; Harnett, Nathaniel G.; Lebois, Lauren A.M.; van Rooij, Sanne J.H.; Ely, Timothy D.; Roeckner, Alyssa; Vincent, Nico; Beaudoin, Francesca L.; An, Xinming; Zeng, Donglin; Neylan, Thomas C.; Clifford, Gari D.; Linnstaedt, Sarah D.; Germine, Laura T.; Rauch, Scott L.; Lewandowski, Christopher; Storrow, Alan B.; Hendry, Phyllis L.; Sheikh, Sophia; Musey, Paul I., Jr.; Haran, John P.; Jones, Christopher W.; Punches, Brittany E.; Lyons, Michael S.; Kurz, Michael C.; McGrath, Meghan E.; Pascual, Jose L.; Datner, Elizabeth M.; Chang, Anna M.; Pearson, Claire; Peak, David A.; Domeier, Robert M.; O'Neil, Brian J.; Rathlev, Niels K.; Sanchez, Leon D.; Pietrzak, Robert H.; Joormann, Jutta; Barch, Deanna M.; Pizzagalli, Diego A.; Sheridan, John F.; Luna, Beatriz; Harte, Steven E.; Elliott, James M.; Murty, Vishnu P.; Jovanovic, Tanja; Bruce, Steven E.; House, Stacey L.; Kessler, Ronald C.; Koenen, Karestan C.; McLean, Samuel A.; Ressler, Kerry J.; Emergency Medicine, School of MedicineObjective: Major negative life events, such as trauma exposure, can play a key role in igniting or exacerbating psychopathology. However, few disorders are diagnosed with respect to precipitating events, and the role of these events in the unfolding of new psychopathology is not well understood. The authors conducted a multisite transdiagnostic longitudinal study of trauma exposure and related mental health outcomes to identify neurobiological predictors of risk, resilience, and different symptom presentations. Methods: A total of 146 participants (discovery cohort: N=69; internal replication cohort: N=77) were recruited from emergency departments within 72 hours of a trauma and followed for the next 6 months with a survey, MRI, and physiological assessments. Results: Task-based functional MRI 2 weeks after a motor vehicle collision identified four clusters of individuals based on profiles of neural activity reflecting threat reactivity, reward reactivity, and inhibitory engagement. Three clusters were replicated in an independent sample with a variety of trauma types. The clusters showed different longitudinal patterns of posttrauma symptoms. Conclusions: These findings provide a novel characterization of heterogeneous stress responses shortly after trauma exposure, identifying potential neuroimaging-based biotypes of trauma resilience and psychopathology.Item Development and Validation of a Model to Predict Posttraumatic Stress Disorder and Major Depression After a Motor Vehicle Collision(American Medical Association, 2021) Ziobrowski, Hannah N.; Kennedy, Chris J.; Ustun, Berk; House, Stacey L.; Beaudoin, Francesca L.; An, Xinming; Zeng, Donglin; Bollen, Kenneth A.; Petukhova, Maria; Sampson, Nancy A.; Puac-Polanco, Victor; Lee, Sue; Koenen, Karestan C.; Ressler, Kerry J.; McLean, Samuel A.; Kessler, Ronald C.; AURORA Consortium; Stevens, Jennifer S.; Neylan, Thomas C.; Clifford, Gari D.; Jovanovic, Tanja; Linnstaedt, Sarah D.; Germine, Laura T.; Rauch, Scott L.; Haran, John P.; Storrow, Alan B.; Lewandowski, Christopher; Musey, Paul I., Jr.; Hendry, Phyllis L.; Sheikh, Sophia; Jones, Christopher W.; Punches, Brittany E.; Lyons, Michael S.; Murty, Vishnu P.; McGrath, Meghan E.; Pascual, Jose L.; Seamon, Mark J.; Datner, Elizabeth M.; Chang, Anna M.; Pearson, Claire; Peak, David A.; Jambaulikar, Guruprasad; Merchant, Roland C.; Domeier, Robert M.; Rathlev, Niels K.; O'Neil, Brian J.; Sergot, Paulina; Sanchez, Leon D.; Bruce, Steven E.; Pietrzak, Robert H.; Joormann, Jutta; Barch, Deanna M.; Pizzagalli, Diego A.; Sheridan, John F.; Harte, Steven E.; Elliott, James M.; van Rooij, Sanne J.H.; Emergency Medicine, School of MedicineImportance: A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions. Objectives: To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later. Design, setting, and participants: The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021. Main outcomes and measures: The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE. Results: A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms. Conclusions and relevance: The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.Item Estimating causal log-odds ratio using the case-control sample and its application in the pharmaco-epidemiology study(Sage, 2018) Zhu, Anqi; Zeng, Donglin; Zhang, Pengyue; Li, Lang; Medical and Molecular Genetics, School of MedicineOne important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases. Data analysis method needs to handle a large sample size that cannot be handled using existing statistical analysis packages. In this paper, we tackle these challenges both methodologically and computationally. We propose a conditional causal log-odds ratio (OR) definition to characterize causal effects of drug exposures on a binary adverse drug event adjusting for individual level confounders. Using a case-control design, we present a propensity score estimation using only case samples and we provide sufficient conditions for the consistency of the estimation of the causal log-odds ratio using case-based propensity scores. Computationally, we implement a principle component analysis to reduce high-dimensional confounders. Extensive simulation studies are performed to demonstrate superior performance of our method to existing methods. Finally, we apply the proposed method to analyze drug-induced myopathy data sampled from a de-identified subset of medical record database (close to 5 million patient records), The Indiana Network for Patient Care. Our method identified 70 drug-induced myopathy (p < 0.05) out 72 drugs, which have myoathy side effects on their FDA drug labels. These 70 drugs include three statins who are known for their myopathy side effects.Item Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy(Wiley, 2018-02-20) Wang, Xueying; Zhang, Pengyue; Chiang, Chien-Wei; Wu, Hengyi; Shen, Li; Ning, Xia; Zeng, Donglin; Wang, Lei; Quinney, Sara K.; Feng, Weixing; Li, Lang; Radiology and Imaging Sciences, School of MedicineDrug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies.Item Neurocognition after motor vehicle collision and adverse post-traumatic neuropsychiatric sequelae within 8 weeks: Initial findings from the AURORA study(Elsevier, 2022) Germine, Laura T.; Joormann, Jutta; Passell, Eliza; Rutter, Lauren A.; Scheuer, Luke; Martini, Paolo; Hwang, Irving; Lee, Sue; Sampson, Nancy; Barch, Deanna M.; House, Stacey L.; Beaudoin, Francesca L.; An, Xinming; Stevens, Jennifer S.; Zeng, Donglin; Linnstaedt, Sarah D.; Jovanovic, Tanja; Clifford, Gari D.; Neylan, Thomas C.; Rauch, Scott L.; Lewandowski, Christopher; Hendry, Phyllis L.; Sheikh, Sophia; Storrow, Alan B.; Musey, Paul I.; Jones, Christopher W.; Punches, Brittney E.; McGrath, Meghan E.; Pascual, Jose L.; Mohiuddin, Kamran; Pearson, Claire; Peak, David A.; Domeier, Robert M.; Bruce, Steven E.; Rathlev, Niels K.; Sanchez, Leon D.; Pietrzak, Robert H.; Pizzagalli, Diego A.; Harte, Steven E.; Elliott, James M.; Koenen, Karesten C.; Ressler, Kerry J.; McLean, Samuel A.; Kessler, Ronald C.; Emergency Medicine, School of MedicineBackground: Previous work has indicated that differences in neurocognitive functioning may predict the development of adverse post-traumatic neuropsychiatric sequelae (APNS). Such differences may be vulnerability factors or simply correlates of APNS-related symptoms. Longitudinal studies that measure neurocognitive functioning at the time of trauma are needed to determine whether such differences precede the development of APNS. Methods: Here, we present findings from a subsample of 666 ambulatory patients from the AURORA (Advancing Understanding of RecOvery afteR trumA) study. All patients presented to EDs after a motor vehicle collision (MVC). We examined associations of neurocognitive test performance shortly after MVC with peritraumatic symptoms in the ED and APNS (depression, post-traumatic stress, post-concussive symptoms, and pain) 2 weeks and 8 weeks later. Neurocognitive tests assessed processing speed, attention, verbal reasoning, memory, and social perception. Results: Distress in the ED was associated with poorer processing speed and short-term memory. Poorer short-term memory was also associated with depression at 2 weeks post-MVC, even after controlling for peritraumatic distress. Finally, higher vocabulary scores were associated with pain 2 weeks post-MVC. Limitations: Self-selection biases among those who present to the ED and enroll in the study limit generalizability. Also, it is not clear whether observed neurocognitive differences predate MVC exposure or arise in the immediate aftermath of MVC exposure. Conclusions: Our results suggest that processing speed and short-term memory may be useful predictors of trauma-related characteristics and the development of some APNS, making such measures clinically-relevant for identifying at-risk individuals.Item Post-traumatic stress and future substance use outcomes: leveraging antecedent factors to stratify risk(Frontiers Media, 2024-03-08) Garrison-Desany, Henri M.; Meyers, Jacquelyn L.; Linnstaedt, Sarah D.; House, Stacey L.; Beaudoin, Francesca L.; An, Xinming; Zeng, Donglin; Neylan, Thomas C.; Clifford, Gari D.; Jovanovic, Tanja; Germine, Laura T.; Bollen, Kenneth A.; Rauch, Scott L.; Haran, John P.; Storrow, Alan B.; Lewandowski, Christopher; Musey, Paul I., Jr.; Hendry, Phyllis L.; Sheikh, Sophia; Jones, Christopher W.; Punches, Brittany E.; Swor, Robert A.; Gentile, Nina T.; Hudak, Lauren A.; Pascual, Jose L.; Seamon, Mark J.; Harris, Erica; Pearson, Claire; Peak, David A.; Domeier, Robert M.; Rathlev, Niels K.; O’Neil, Brian J.; Sergot, Paulina; Sanchez, Leon D.; Bruce, Steven E.; Joormann, Jutta; Harte, Steven E.; McLean, Samuel A.; Koenen, Karestan C.; Denckla, Christy A.; Emergency Medicine, School of MedicineBackground: Post-traumatic stress disorder (PTSD) and substance use (tobacco, alcohol, and cannabis) are highly comorbid. Many factors affect this relationship, including sociodemographic and psychosocial characteristics, other prior traumas, and physical health. However, few prior studies have investigated this prospectively, examining new substance use and the extent to which a wide range of factors may modify the relationship to PTSD. Methods: The Advancing Understanding of RecOvery afteR traumA (AURORA) study is a prospective cohort of adults presenting at emergency departments (N = 2,943). Participants self-reported PTSD symptoms and the frequency and quantity of tobacco, alcohol, and cannabis use at six total timepoints. We assessed the associations of PTSD and future substance use, lagged by one timepoint, using the Poisson generalized estimating equations. We also stratified by incident and prevalent substance use and generated causal forests to identify the most important effect modifiers of this relationship out of 128 potential variables. Results: At baseline, 37.3% (N = 1,099) of participants reported likely PTSD. PTSD was associated with tobacco frequency (incidence rate ratio (IRR): 1.003, 95% CI: 1.00, 1.01, p = 0.02) and quantity (IRR: 1.01, 95% CI: 1.001, 1.01, p = 0.01), and alcohol frequency (IRR: 1.002, 95% CI: 1.00, 1.004, p = 0.03) and quantity (IRR: 1.003, 95% CI: 1.001, 1.01, p = 0.001), but not with cannabis use. There were slight differences in incident compared to prevalent tobacco frequency and quantity of use; prevalent tobacco frequency and quantity were associated with PTSD symptoms, while incident tobacco frequency and quantity were not. Using causal forests, lifetime worst use of cigarettes, overall self-rated physical health, and prior childhood trauma were major moderators of the relationship between PTSD symptoms and the three substances investigated. Conclusion: PTSD symptoms were highly associated with tobacco and alcohol use, while the association with prospective cannabis use is not clear. Findings suggest that understanding the different risk stratification that occurs can aid in tailoring interventions to populations at greatest risk to best mitigate the comorbidity between PTSD symptoms and future substance use outcomes. We demonstrate that this is particularly salient for tobacco use and, to some extent, alcohol use, while cannabis is less likely to be impacted by PTSD symptoms across the strata.
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